Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does well: it discloses that scoring is against real NWS station observations, that versions are never mixed, that each cell includes a raw-NWS baseline MAE on comparable rows and sample counts, and that granularity is daily. It also surfaces the $0.03 x402 pricing and HTTP GET transport. It stops short of stating auth/rate-limit behavior or response format, so not a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Key content is front-loaded in the first sentence, and the audit-trail framing is useful. There is mild redundancy between 'per station, lead, method, version' and 'per station × lead × method segment × model version,' but the sentences largely earn their place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description must convey returns, and it does: MAE, signed bias, NWS baseline MAE, and sample counts per cell. Combined with the pricing and transport note, an agent has enough to call it correctly, with the only gap being full parameter/enum coverage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 50% (segment and lead_hours are undocumented in the schema), and the description compensates by explaining the aggregation dimensions as 'station × lead × method segment × model version,' which clarifies what the segment and lead_hours parameters select. It still omits explicit guidance on valid lead_hours ranges or segment enum meaning, so it adds real but not complete value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The opening line states exactly what is returned ('daily MAE/bias per station, lead, method, version') and the description positions it as the audit table behind the free verification summary, which cleanly separates it from weather_verification-scores and weather_verification-station. An agent can tell what this tool produces without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description notes it is the raw audit table 'behind our free verification summary,' which implies the summary sibling is the lighter-weight alternative, but it never explicitly says when to choose one over the other. There are no stated prerequisites, exclusions, or routing instructions beyond this implication.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.